Contexte

Objective:
To analyze lysosomes, cellular organelles, focusing on cathepsins and lipofuscins, to understand cellular changes associated with specific pathologies, particularly Alzheimer’s disease (APP vs. CTRL).

Methodology:

Definition of Lysosomes :
- Cellular organelles responsible for intracellular digestion
- The cathepsin, a lysosomal protein, is under investigation
Labeling of Cathepsin B:
- Use of the clipping method (3 or 4 pixels)
- Testing processing with a size of 3px to assess the impact on puncta
Clinical Groups:
- The clinical groups include CTRL, DS, DSD, and APP
- Multiple images per individual, multiple cells per image
Variables to Explain:
- Cellular changes in relation to the area
- Number of structures (cathepsins and lipofuscins) standardized by the region of interest (ROI, area)

Analysis of Cell Files:

The files show the outline and interior of the cells
Perimeter in square micrometers obtained by multiplying the outline by a constant (0.08154)
The area obtained by multiplying the interior by the constant squared

Hypothesis:

The patients, especially those with Alzheimer’s disease (APP), have larger cell areas due to the increased size of lysosomes (cell detoxification due to the disease)

Study Limitations:

Multicentric study involving brains of varying ages and quality
Non-pooled gender
Possibility of variations due to the waiting time between the subject’s death and the preservation of the sections

Methods used

Statistical Methods Used: We applied mixed-effects linear models to analyze the data, taking into account several variables of interest for each of the six groups.

Area of each cathepsin:
- Fixed effects: Group
- Random effects: Structured effect for different cells per subject and multiple cathepsins per cell
Cell outline:
- Fixed effects: Group
- Random effects: Structured effect for different cells per subject
Number of points per cell size:
- Fixed effects: Group
- Random effects: Structured effect for different cells per subject

Equations associated with these models:

Area of each cathepsin: \[ Y_{ijk} = \beta_{0} + \beta_{\text{group(i)}} + u_{\text{cell (ij)}} + v_{\text{subjet (i)}} + \varepsilon_{ijk} \]
Cell outline: \[ Y_{ij} = \beta_{0} + \beta_{\text{group (i)}} + u_{\text{subjet (i)}} + \varepsilon_{ij} \]
Number of points per cell size: \[ Y_{ij} = \beta_{0} + \beta_{\text{group (i)}} + u_{\text{subjet (i)}} + \varepsilon_{ij} \]

Where:

\(Y_{ijk}\) represents the response variable for observation \(ijk\), with \(i\) being the subject index, \(j\) the cell index, and \(k\) the cathepsin index,
\(\beta\) are the coefficients for group-specific fixed effects,
\(u\), \(v\), and \(w\) are random effects,
\(\varepsilon_{ijk}\) is the error term for observation \(ijk\) in group \(k\).

Data management

The first ten lines of the database to have an overview of the data we will analyze.

data <- openxlsx::read.xlsx("/home/baptiste.criniere/Documents/PB_AD_AK/Data/icy_size3.xlsx", sheet = 1)

data <- data %>% 
  dplyr::select(brain_id, disease_grp, image_id, cell_id, modality, area_um2, nb_poncta_area_um2_weighted) %>% 
  dplyr::mutate(brain_id = brain_id %>% factor) %>% 
  dplyr::mutate(disease_grp = disease_grp %>% factor) %>% 
  dplyr::mutate(image_id = image_id %>% factor) %>%
  dplyr::mutate(cell_id = cell_id %>% factor) %>%
  dplyr::mutate(modality = modality %>% factor) %>% 
  dplyr::rename(nb_poncta_area = nb_poncta_area_um2_weighted) %>% 
  dplyr::rename(area = area_um2)

demo <- openxlsx::read.xlsx("/home/baptiste.criniere/Documents/PB_AD_AK/Data/Demographics_11-2021_Amal_modified.xlsx", sheet = 3, startRow = 2)

demo <- demo %>% 
  dplyr::select(EXCEL, BRAINBANK) %>% 
  dplyr::rename(brain_id = EXCEL)


data <- data %>% 
  dplyr::left_join(demo)

head(data, n = 10) %>% 
  DT::datatable() %>% 
  DT::formatRound(columns=c('area', 'nb_poncta_area'), digits=3)

Contour

Representation by group

Here is an interactive figure where each point corresponds to the averaged area for a cell from the same subject

fig <- data %>% 
  dplyr::filter(modality %in% "contour") %>% 
  dplyr::group_by(brain_id, disease_grp) %>% 
  dplyr::summarise(Area = mean(area, na.rm = TRUE)) %>% 
  ggplot(aes(x = disease_grp, y = Area))+
  geom_boxplot(outlier.shape = NA, alpha = 0.5)+
  geom_jitter(alpha = 0.5, size = 2)+
  theme_classic()+
  theme(legend.position = "none")+
  labs(x = "")

ggplotly(fig)

Represntation by group and center

As observed in this figure, each center contains only one pathology. Therefore, disregarding the centers in our analysis could introduce bias. If we measure the effect of the disease on the area, we might actually be observing the effect of the center. In the following section, we will conduct an analysis on the controls to see if there is a center effect.

data %>% 
  dplyr::filter(modality %in% "contour") %>% 
  dplyr::group_by(brain_id, disease_grp, BRAINBANK) %>% 
  dplyr::summarise(Area = mean(area, na.rm = TRUE)) %>%
  dplyr::filter(!is.na(BRAINBANK)) %>% 
  ggplot(aes(x = disease_grp, y = Area, color = disease_grp))+
  geom_boxplot(outlier.shape = NA, alpha = 0.5)+
  geom_jitter(alpha = 0.5, size = 2)+
  facet_wrap(~BRAINBANK)+
  theme_classic()+
  theme(legend.position = "none")+
  scale_color_aaas()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x = "")

All CATHB

Representation of raw data

data %>% 
  dplyr::filter(modality %in% "all CATHB") %>% 
  dplyr::filter(area < 1) %>% 
  dplyr::arrange(disease_grp, brain_id) %>%
  ggplot(aes(x = reorder(brain_id, as.numeric(disease_grp), na.rm = TRUE), y = area, color = disease_grp))+
  geom_boxplot(outlier.shape = NA)+
  geom_jitter(alpha = 0.075, size = 0.1)+
  theme_classic()+
  labs(x = "", color = "Disease")+
  scale_color_nejm()+
  scale_y_continuous(expand = c(0.001,0.01))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        legend.position = "bottom")+
  guides(color = guide_legend(nrow = 1))

Representation by group

Here is an interactive figure where each point corresponds to the averaged area for a cell from the same subject

fig <- data %>% 
  dplyr::filter(modality %in% "all CATHB") %>% 
  dplyr::group_by(brain_id, disease_grp) %>% 
  dplyr::summarise(Area = mean(area, na.rm = TRUE)) %>% 
  ggplot(aes(x = disease_grp, y = Area))+
  geom_boxplot(outlier.shape = NA, alpha = 0.5)+
  geom_jitter(alpha = 0.5, size = 2)+
  theme_classic()+
  theme(legend.position = "none")+
  labs(x = "")

ggplotly(fig)

Represntation by group and center

As observed in this figure, each center contains only one pathology. Therefore, disregarding the centers in our analysis could introduce bias. If we measure the effect of the disease on the area, we might actually be observing the effect of the center. In the following section, we will conduct an analysis on the controls to see if there is a center effect.

data %>% 
  dplyr::filter(modality %in% "all CATHB") %>% 
  dplyr::group_by(brain_id, disease_grp, BRAINBANK) %>% 
  dplyr::summarise(Area = mean(area, na.rm = TRUE)) %>%
  dplyr::filter(!is.na(BRAINBANK)) %>% 
  ggplot(aes(x = disease_grp, y = Area, color = disease_grp))+
  geom_boxplot(outlier.shape = NA, alpha = 0.5)+
  geom_jitter(alpha = 0.5, size = 2)+
  facet_wrap(~BRAINBANK)+
  theme_classic()+
  theme(legend.position = "none")+
  scale_color_aaas()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x = "")

LF

Representation of raw data

data %>% 
  dplyr::filter(modality %in% "LF") %>% 
  dplyr::filter(area < 1) %>% 
  dplyr::arrange(disease_grp, brain_id) %>%
  ggplot(aes(x = reorder(brain_id, as.numeric(disease_grp), na.rm = TRUE), y = area, color = disease_grp))+
  geom_boxplot(outlier.shape = NA)+
  geom_jitter(alpha = 0.075, size = 0.1)+
  theme_classic()+
  labs(x = "", color = "Disease")+
  scale_color_nejm()+
  scale_y_continuous(expand = c(0.001,0.01))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        legend.position = "bottom")+
  guides(color = guide_legend(nrow = 1))

Representation by group

Here is an interactive figure where each point corresponds to the averaged area for a cell from the same subject

fig <- data %>% 
  dplyr::filter(modality %in% "LF") %>% 
  dplyr::group_by(brain_id, disease_grp) %>% 
  dplyr::summarise(Area = mean(area, na.rm = TRUE)) %>% 
  ggplot(aes(x = disease_grp, y = Area))+
  geom_boxplot(outlier.shape = NA, alpha = 0.5)+
  geom_jitter(alpha = 0.5, size = 2)+
  theme_classic()+
  theme(legend.position = "none")+
  labs(x = "")

ggplotly(fig)

Represntation by group and center

As observed in this figure, each center contains only one pathology. Therefore, disregarding the centers in our analysis could introduce bias. If we measure the effect of the disease on the area, we might actually be observing the effect of the center. In the following section, we will conduct an analysis on the controls to see if there is a center effect.

data %>% 
  dplyr::filter(modality %in% "LF") %>% 
  dplyr::group_by(brain_id, disease_grp, BRAINBANK) %>% 
  dplyr::summarise(Area = mean(area, na.rm = TRUE)) %>%
  dplyr::filter(!is.na(BRAINBANK)) %>% 
  ggplot(aes(x = disease_grp, y = Area, color = disease_grp))+
  geom_boxplot(outlier.shape = NA, alpha = 0.5)+
  geom_jitter(alpha = 0.5, size = 2)+
  facet_wrap(~BRAINBANK)+
  theme_classic()+
  theme(legend.position = "none")+
  scale_color_aaas()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x = "")

excl CATHB

Representation of raw data

data %>% 
  dplyr::filter(modality %in% "excl CATHB") %>% 
  dplyr::filter(area < 1) %>% 
  dplyr::arrange(disease_grp, brain_id) %>%
  ggplot(aes(x = reorder(brain_id, as.numeric(disease_grp), na.rm = TRUE), y = area, color = disease_grp))+
  geom_boxplot(outlier.shape = NA)+
  geom_jitter(alpha = 0.075, size = 0.1)+
  theme_classic()+
  labs(x = "", color = "Disease")+
  scale_color_nejm()+
  scale_y_continuous(expand = c(0.001,0.01))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        legend.position = "bottom")+
  guides(color = guide_legend(nrow = 1))

Representation by group

Here is an interactive figure where each point corresponds to the averaged area for a cell from the same subject

fig <- data %>% 
  dplyr::filter(modality %in% "excl CATHB") %>% 
  dplyr::group_by(brain_id, disease_grp) %>% 
  dplyr::summarise(Area = mean(area, na.rm = TRUE)) %>% 
  ggplot(aes(x = disease_grp, y = Area))+
  geom_boxplot(outlier.shape = NA, alpha = 0.5)+
  geom_jitter(alpha = 0.5, size = 2)+
  theme_classic()+
  theme(legend.position = "none")+
  labs(x = "")

ggplotly(fig)

Represntation by group and center

As observed in this figure, each center contains only one pathology. Therefore, disregarding the centers in our analysis could introduce bias. If we measure the effect of the disease on the area, we might actually be observing the effect of the center. In the following section, we will conduct an analysis on the controls to see if there is a center effect.

data %>% 
  dplyr::filter(modality %in% "excl CATHB") %>% 
  dplyr::group_by(brain_id, disease_grp, BRAINBANK) %>% 
  dplyr::summarise(Area = mean(area, na.rm = TRUE)) %>%
  dplyr::filter(!is.na(BRAINBANK)) %>% 
  ggplot(aes(x = disease_grp, y = Area, color = disease_grp))+
  geom_boxplot(outlier.shape = NA, alpha = 0.5)+
  geom_jitter(alpha = 0.5, size = 2)+
  facet_wrap(~BRAINBANK)+
  theme_classic()+
  theme(legend.position = "none")+
  scale_color_aaas()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x = "")

minus LF

Representation of raw data

data %>% 
  dplyr::filter(modality %in% "minus LF") %>% 
  dplyr::filter(area < 1) %>% 
  dplyr::arrange(disease_grp, brain_id) %>%
  ggplot(aes(x = reorder(brain_id, as.numeric(disease_grp), na.rm = TRUE), y = area, color = disease_grp))+
  geom_boxplot(outlier.shape = NA)+
  geom_jitter(alpha = 0.075, size = 0.1)+
  theme_classic()+
  labs(x = "", color = "Disease")+
  scale_color_nejm()+
  scale_y_continuous(expand = c(0.001,0.01))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        legend.position = "bottom")+
  guides(color = guide_legend(nrow = 1))

Representation by group

Here is an interactive figure where each point corresponds to the averaged area for a cell from the same subject

fig <- data %>% 
  dplyr::filter(modality %in% "minus LF") %>% 
  dplyr::group_by(brain_id, disease_grp) %>% 
  dplyr::summarise(Area = mean(area, na.rm = TRUE)) %>% 
  ggplot(aes(x = disease_grp, y = Area))+
  geom_boxplot(outlier.shape = NA, alpha = 0.5)+
  geom_jitter(alpha = 0.5, size = 2)+
  theme_classic()+
  theme(legend.position = "none")+
  labs(x = "")

ggplotly(fig)

Represntation by group and center

As observed in this figure, each center contains only one pathology. Therefore, disregarding the centers in our analysis could introduce bias. If we measure the effect of the disease on the area, we might actually be observing the effect of the center. In the following section, we will conduct an analysis on the controls to see if there is a center effect.

data %>% 
  dplyr::filter(modality %in% "minus LF") %>% 
  dplyr::group_by(brain_id, disease_grp, BRAINBANK) %>% 
  dplyr::summarise(Area = mean(area, na.rm = TRUE)) %>%
  dplyr::filter(!is.na(BRAINBANK)) %>% 
  ggplot(aes(x = disease_grp, y = Area, color = disease_grp))+
  geom_boxplot(outlier.shape = NA, alpha = 0.5)+
  geom_jitter(alpha = 0.5, size = 2)+
  facet_wrap(~BRAINBANK)+
  theme_classic()+
  theme(legend.position = "none")+
  scale_color_aaas()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x = "")

Contour

Center effect on the controls

No center effect is observed on the controls, and we assume that this conclusion may extend to the other groups. Therefore, we proceed with the rest of the analysis without considering a center effect

data2 <- data %>% 
  dplyr::filter(disease_grp %in% "CTRL" & modality %in% "contour")

model <- lmerTest::lmer(area ~ BRAINBANK + (1|brain_id), data = data2)
Anova(model)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: area
##            Chisq Df Pr(>Chisq)  
## BRAINBANK 8.5577  2    0.01386 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modelling

data2 <- data %>% 
  dplyr::filter(modality %in% "contour")
model <- lmerTest::lmer(area ~ disease_grp + (1|brain_id), data = data2)
Anova(model)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: area
##              Chisq Df Pr(>Chisq)  
## disease_grp 10.724  5    0.05714 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

emm <- emmeans::emmeans(model,  pairwise ~ "disease_grp")
emm1 <- emm$emmeans

emm1 <- data.frame(emm1) %>% 
  dplyr::select(disease_grp, emmean, SE)

emm$contrasts

##  contrast      estimate   SE   df t.ratio p.value
##  APP - CTRL      -12.11 21.8 49.0  -0.556  0.9933
##  APP - DS        -73.06 31.5 48.9  -2.323  0.2048
##  APP - DSD       -31.75 27.3 48.0  -1.161  0.8528
##  APP - DUPAPP    -34.42 26.2 48.6  -1.315  0.7755
##  APP - SAD        13.63 22.8 48.5   0.599  0.9906
##  CTRL - DS       -60.95 29.8 48.3  -2.046  0.3324
##  CTRL - DSD      -19.64 25.4 47.1  -0.773  0.9708
##  CTRL - DUPAPP   -22.31 24.2 47.7  -0.924  0.9384
##  CTRL - SAD       25.74 20.4 47.2   1.262  0.8035
##  DS - DSD         41.31 34.1 47.9   1.212  0.8289
##  DS - DUPAPP      38.64 33.2 48.2   1.166  0.8508
##  DS - SAD         86.69 30.5 48.1   2.840  0.0680
##  DSD - DUPAPP     -2.67 29.3 47.2  -0.091  1.0000
##  DSD - SAD        45.39 26.3 46.9   1.728  0.5211
##  DUPAPP - SAD     48.05 25.1 47.4   1.918  0.4042
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 6 estimates

emm1 %>% 
  ggplot(aes(x = disease_grp, y = emmean))+
  geom_bar(stat = "identity", color="black", width = 0.85, alpha = 0.75)+
  geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), width=.2,
                position=position_dodge(.9))+
  theme_classic()+
  labs(x = "", y = "Estimated mean")

All CATHB

Center effect on the controls

No center effect is observed on the controls, and we assume that this conclusion may extend to the other groups. Therefore, we proceed with the rest of the analysis without considering a center effect

data2 <- data %>% 
  dplyr::filter(disease_grp %in% "CTRL" & modality %in% "all CATHB")

model <- lmerTest::lmer(area ~ BRAINBANK + (1|brain_id) + (1|cell_id), data = data2)
Anova(model)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: area
##            Chisq Df Pr(>Chisq)
## BRAINBANK 0.6284  2     0.7304

Modelling

data2 <- data %>% 
  dplyr::filter(modality %in% "all CATHB")
model <- lmerTest::lmer(area ~ disease_grp + (1|brain_id) + (1|cell_id), data = data2)
Anova(model)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: area
##              Chisq Df Pr(>Chisq)  
## disease_grp 10.776  5      0.056 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

emm <- emmeans::emmeans(model,  pairwise ~ "disease_grp")
emm1 <- emm$emmeans

emm1 <- data.frame(emm1) %>% 
  dplyr::select(disease_grp, emmean, SE)

emm$contrasts

##  contrast       estimate      SE  df z.ratio p.value
##  APP - CTRL    -0.021627 0.01019 Inf  -2.123  0.2754
##  APP - DS      -0.000241 0.01469 Inf  -0.016  1.0000
##  APP - DSD      0.007607 0.01286 Inf   0.591  0.9916
##  APP - DUPAPP   0.000404 0.01226 Inf   0.033  1.0000
##  APP - SAD     -0.016937 0.01068 Inf  -1.586  0.6078
##  CTRL - DS      0.021386 0.01384 Inf   1.545  0.6348
##  CTRL - DSD     0.029234 0.01189 Inf   2.459  0.1364
##  CTRL - DUPAPP  0.022031 0.01123 Inf   1.961  0.3648
##  CTRL - SAD     0.004690 0.00948 Inf   0.495  0.9964
##  DS - DSD       0.007848 0.01591 Inf   0.493  0.9964
##  DS - DUPAPP    0.000645 0.01543 Inf   0.042  1.0000
##  DS - SAD      -0.016696 0.01420 Inf  -1.175  0.8487
##  DSD - DUPAPP  -0.007203 0.01371 Inf  -0.526  0.9952
##  DSD - SAD     -0.024544 0.01231 Inf  -1.994  0.3457
##  DUPAPP - SAD  -0.017341 0.01168 Inf  -1.485  0.6742
## 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: tukey method for comparing a family of 6 estimates

emm1 %>% 
  ggplot(aes(x = disease_grp, y = emmean))+
  geom_bar(stat = "identity", color="black", width = 0.85, alpha = 0.75)+
  geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), width=.2,
                position=position_dodge(.9))+
  theme_classic()+
  ylim(c(0,0.2))+
  labs(x = "", y = "Estimated mean")

LF

Center effect on the controls

No center effect is observed on the controls, and we assume that this conclusion may extend to the other groups. Therefore, we proceed with the rest of the analysis without considering a center effect

data2 <- data %>% 
  dplyr::filter(disease_grp %in% "CTRL" & modality %in% "LF")

model <- lmerTest::lmer(area ~ BRAINBANK + (1|brain_id) + (1|cell_id), data = data2)
Anova(model)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: area
##            Chisq Df Pr(>Chisq)  
## BRAINBANK 5.0367  2    0.08059 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modelling

data2 <- data %>% 
  dplyr::filter(modality %in% "LF")
model <- lmerTest::lmer(area ~ disease_grp + (1|brain_id) + (1|cell_id), data = data2)
Anova(model)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: area
##              Chisq Df Pr(>Chisq)   
## disease_grp 16.765  5   0.004968 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

emm <- emmeans::emmeans(model,  pairwise ~ "disease_grp")
emm1 <- emm$emmeans

emm1 <- data.frame(emm1) %>% 
  dplyr::select(disease_grp, emmean, SE)

emm$contrasts

##  contrast      estimate     SE  df z.ratio p.value
##  APP - CTRL     0.00701 0.0293 Inf   0.239  0.9999
##  APP - DS       0.08636 0.0421 Inf   2.050  0.3139
##  APP - DSD      0.11849 0.0369 Inf   3.211  0.0166
##  APP - DUPAPP   0.07153 0.0352 Inf   2.030  0.3249
##  APP - SAD      0.03379 0.0307 Inf   1.101  0.8812
##  CTRL - DS      0.07935 0.0399 Inf   1.987  0.3495
##  CTRL - DSD     0.11148 0.0344 Inf   3.244  0.0150
##  CTRL - DUPAPP  0.06452 0.0326 Inf   1.981  0.3533
##  CTRL - SAD     0.02678 0.0276 Inf   0.970  0.9274
##  DS - DSD       0.03213 0.0458 Inf   0.702  0.9818
##  DS - DUPAPP   -0.01484 0.0444 Inf  -0.334  0.9995
##  DS - SAD      -0.05257 0.0410 Inf  -1.284  0.7942
##  DSD - DUPAPP  -0.04696 0.0395 Inf  -1.188  0.8428
##  DSD - SAD     -0.08470 0.0356 Inf  -2.382  0.1624
##  DUPAPP - SAD  -0.03774 0.0338 Inf  -1.116  0.8751
## 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: tukey method for comparing a family of 6 estimates

emm1 %>% 
  ggplot(aes(x = disease_grp, y = emmean))+
  geom_bar(stat = "identity", color="black", width = 0.85, alpha = 0.75)+
  geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), width=.2,
                position=position_dodge(.9))+
  theme_classic()+
  ylim(c(0,0.4))+
  labs(x = "", y = "Estimated mean")

excl CATHB

Center effect on the controls

No center effect is observed on the controls, and we assume that this conclusion may extend to the other groups. Therefore, we proceed with the rest of the analysis without considering a center effect

data2 <- data %>% 
  dplyr::filter(disease_grp %in% "CTRL" & modality %in% "excl CATHB")

model <- lmerTest::lmer(area ~ BRAINBANK + (1|brain_id) + (1|cell_id), data = data2)
Anova(model)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: area
##            Chisq Df Pr(>Chisq)
## BRAINBANK 0.6122  2     0.7363

Modelling

data2 <- data %>% 
  dplyr::filter(modality %in% "excl CATHB")
model <- lmerTest::lmer(area ~ disease_grp + (1|brain_id) + (1|cell_id), data = data2)
Anova(model)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: area
##              Chisq Df Pr(>Chisq)  
## disease_grp 10.153  5    0.07102 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

emm <- emmeans::emmeans(model,  pairwise ~ "disease_grp")
emm1 <- emm$emmeans

emm1 <- data.frame(emm1) %>% 
  dplyr::select(disease_grp, emmean, SE)

emm$contrasts

##  contrast      estimate      SE  df z.ratio p.value
##  APP - CTRL    -0.01881 0.01022 Inf  -1.841  0.4395
##  APP - DS       0.00107 0.01471 Inf   0.073  1.0000
##  APP - DSD      0.00776 0.01291 Inf   0.601  0.9910
##  APP - DUPAPP   0.00522 0.01235 Inf   0.423  0.9983
##  APP - SAD     -0.01602 0.01071 Inf  -1.495  0.6672
##  CTRL - DS      0.01988 0.01384 Inf   1.437  0.7045
##  CTRL - DSD     0.02657 0.01190 Inf   2.232  0.2230
##  CTRL - DUPAPP  0.02403 0.01129 Inf   2.129  0.2720
##  CTRL - SAD     0.00279 0.00947 Inf   0.294  0.9997
##  DS - DSD       0.00669 0.01593 Inf   0.420  0.9983
##  DS - DUPAPP    0.00415 0.01548 Inf   0.268  0.9998
##  DS - SAD      -0.01710 0.01421 Inf  -1.203  0.8355
##  DSD - DUPAPP  -0.00254 0.01377 Inf  -0.184  1.0000
##  DSD - SAD     -0.02378 0.01233 Inf  -1.929  0.3844
##  DUPAPP - SAD  -0.02125 0.01174 Inf  -1.810  0.4592
## 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: tukey method for comparing a family of 6 estimates

emm1 %>% 
  ggplot(aes(x = disease_grp, y = emmean))+
  geom_bar(stat = "identity", color="black", width = 0.85, alpha = 0.75)+
  geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), width=.2,
                position=position_dodge(.9))+
  theme_classic()+
  ylim(c(0,0.2))+
  labs(x = "", y = "Estimated mean")

minus LF

Center effect on the controls

No center effect is observed on the controls, and we assume that this conclusion may extend to the other groups. Therefore, we proceed with the rest of the analysis without considering a center effect

data2 <- data %>% 
  dplyr::filter(disease_grp %in% "CTRL" & modality %in% "minus LF")

model <- lmerTest::lmer(area ~ BRAINBANK + (1|brain_id) + (1|cell_id), data = data2)
Anova(model)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: area
##           Chisq Df Pr(>Chisq)
## BRAINBANK 0.616  2     0.7349

Modelling

data2 <- data %>% 
  dplyr::filter(modality %in% "minus LF")
model <- lmerTest::lmer(area ~ disease_grp + (1|brain_id) + (1|cell_id), data = data2)
Anova(model)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: area
##              Chisq Df Pr(>Chisq)  
## disease_grp 10.752  5    0.05653 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

emm <- emmeans::emmeans(model,  pairwise ~ "disease_grp")
emm1 <- emm$emmeans

emm1 <- data.frame(emm1) %>% 
  dplyr::select(disease_grp, emmean, SE)

emm$contrasts

##  contrast      estimate      SE  df z.ratio p.value
##  APP - CTRL    -0.02000 0.01019 Inf  -1.962  0.3642
##  APP - DS      -0.00128 0.01469 Inf  -0.087  1.0000
##  APP - DSD      0.00762 0.01288 Inf   0.592  0.9916
##  APP - DUPAPP   0.00457 0.01231 Inf   0.371  0.9991
##  APP - SAD     -0.01701 0.01069 Inf  -1.592  0.6041
##  CTRL - DS      0.01872 0.01383 Inf   1.353  0.7549
##  CTRL - DSD     0.02762 0.01189 Inf   2.323  0.1847
##  CTRL - DUPAPP  0.02457 0.01127 Inf   2.180  0.2471
##  CTRL - SAD     0.00299 0.00947 Inf   0.316  0.9996
##  DS - DSD       0.00890 0.01592 Inf   0.559  0.9936
##  DS - DUPAPP    0.00585 0.01546 Inf   0.378  0.9990
##  DS - SAD      -0.01573 0.01420 Inf  -1.108  0.8785
##  DSD - DUPAPP  -0.00305 0.01375 Inf  -0.222  0.9999
##  DSD - SAD     -0.02463 0.01232 Inf  -2.000  0.3423
##  DUPAPP - SAD  -0.02158 0.01172 Inf  -1.841  0.4392
## 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: tukey method for comparing a family of 6 estimates

emm1 %>% 
  ggplot(aes(x = disease_grp, y = emmean))+
  geom_bar(stat = "identity", color="black", width = 0.85, alpha = 0.75)+
  geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), width=.2,
                position=position_dodge(.9))+
  theme_classic()+
  ylim(c(0,0.2))+
  labs(x = "", y = "Estimated mean")

Analyse

Unpublished work, inconclusive results

Baptiste Crinière-Boizet

Contexte

Methods used

Data management

Analyse

Unpublished work, inconclusive results

Baptiste Crinière-Boizet

Contexte

Methods used

Data management

Data visualisation

Contour

Representation by group

Represntation by group and center

All CATHB

Representation of raw data

Representation by group

Represntation by group and center

LF

Representation of raw data

Representation by group

Represntation by group and center

excl CATHB

Representation of raw data

Representation by group

Represntation by group and center

minus LF

Representation of raw data

Representation by group

Represntation by group and center

Modelling

Contour

Center effect on the controls

Modelling

All CATHB

Center effect on the controls

Modelling

LF

Center effect on the controls

Modelling

excl CATHB

Center effect on the controls

Modelling

minus LF

Center effect on the controls

Modelling